The five ways to host an AI application in 2026 — managed model APIs, inference API providers, runtimes in your own cloud account, on-premise and air-gapped — and what you operate versus what the provider operates in each
On ibl.ai you own all the code and the data, run it model-agnostic across any LLM, and pay with no per-seat pricing — so you can deploy anywhere, from your own cloud to a fully air-gapped network.
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Self-hosted AI means the model runtime and the application around it run on infrastructure you control. Managed cloud AI hosting means a provider operates one or both. On ibl.ai you own all the code and the data, and the platform runs on any of the hosting options below.
The options, from most to least managed: managed model APIs (Amazon Bedrock, Azure OpenAI in Microsoft Foundry, Google Vertex AI, the OpenAI API); inference API providers for open-weight models (Together AI, Fireworks, Groq, DeepInfra, Baseten).
Then: open-source runtimes in your own cloud account or VPC (vLLM and SGLang, Apache-2.0; Ollama and llama.cpp, MIT); on-premise GPU servers; and fully air-gapped networks. Hugging Face TGI was archived in March 2026 and now points users to vLLM, SGLang and llama.cpp.
The trade is operations for control. Managed hosting removes GPU work and bills per token; self-hosting takes the provider out of the data path and is the only air-gapped route. ibl.ai is model-agnostic, has no per-seat pricing, and is family-owned and operated from New York, NY.
by ibl.ai
Platform and models on infrastructure you controlby Amazon Bedrock, Azure OpenAI, Google Vertex AI, OpenAI API, inference API providers
Provider-operated model hosting| Criteria | Self-Hosted AI (ibl.ai) | Managed Cloud AI Hosting |
|---|---|---|
| GPUs and model serving | You run vLLM, SGLang, NVIDIA NIM or Ollama on your GPUs — or the platform calls a managed API for that workload. | The provider runs capacity, scaling, model upgrades and failover. |
| Agents, memory, RAG and logs | On ibl.ai, in the database you operate: conversations, memories, datasets and eval traces. | Your app's own store plus provider-side stateful features — OpenAI's Responses API keeps state 30 days by default. |
| Where prompts and outputs travel | Inside your network for self-hosted models; to a provider you choose, per workload, when not. | To the provider's region; Azure Global and DataZone deployments may process in other geographies in the zone. |
| Air-gapped operation | Supported: the full platform with local models and zero external API calls. | Not possible — a managed endpoint needs network reachability. |
| Criteria | Self-Hosted AI (ibl.ai) | Managed Cloud AI Hosting |
|---|---|---|
| Fixed cost to start | GPU servers or reserved cloud instances, paid whether or not they are busy. | Near zero: on-demand per-token pricing with no commitment. |
| Cost at sustained high volume | Flat GPU cost amortised over every token once utilisation is high. | Scales linearly with tokens; Bedrock batch is 50% lower and provisioned throughput is hourly, with 1- or 6-month terms. |
| Seat licensing | ibl.ai has no per-seat pricing; usage against caps you set. | Model APIs are per token, not per seat — the per-seat bill comes from SaaS apps layered on top. |
| Criteria | Self-Hosted AI (ibl.ai) | Managed Cloud AI Hosting |
|---|---|---|
| Frontier proprietary models | Reached through the same managed APIs, routed from your platform; closed weights cannot be self-hosted. | The only place the newest closed models run, often on launch day. |
| Elasticity and time to first deployment | Capacity is the GPUs you provisioned; infra-cli automates the platform install. | An API key and minutes; bursts absorbed by the provider. |
| Model and provider portability | Switch any agent between self-hosted and managed models without rewriting the application. | Bound to one provider's catalog, SDK and stateful features unless you add an abstraction. |
Self-hosted options: open-source runtimes such as vLLM, SGLang, Ollama and llama.cpp in your cloud account or VPC, on-premise GPU servers, and air-gapped networks with no outbound path.
Managed options: hyperscaler model APIs (Amazon Bedrock, Azure OpenAI, Google Vertex AI), the model labs' own APIs, and inference API providers serving open-weight models.
Most enterprises end up using several. The useful decision is per workload, not one hosting model for the whole organization.
Self-hosting has a fixed GPU cost that wins once utilisation is high and steady, because each extra token is close to free.
Managed hosting has no fixed cost and wins for spiky or low volume; Bedrock adds a 50% batch discount and hourly provisioned throughput for steadier loads.
Compare the monthly token bill against the cost of the GPUs that would serve it at your real utilisation. Below that break-even, rent; above it, own.
On ibl.ai the platform — agents, memory, data, audit — stays on your infrastructure while each agent's model can be a local runtime or a managed API.
Hyperscalers support hybrid patterns inside their own cloud, such as VPC endpoints, but the model endpoint remains theirs.
Hybrid is the common answer: own the application layer and the data, and pick the model host per workload and per sensitivity.
No fixed cost and instant capacity make managed model APIs the right first step.
Once GPUs stay busy, a flat hardware cost undercuts per-token pricing, and the data never leaves your network.
No managed endpoint can serve a network with no outbound path; only self-hosted models can.
Own the platform and the data, then host each model wherever its cost, capability and sensitivity point.
Timeline: Two to six weeks for the platform; model moves happen workload by workload
Timeline: Hours per workload
ibl.ai is the agentic AI platform where you own all the code and the data. You self-host the entire stack inside your own perimeter, run it model-agnostic across any LLM and switch anytime, and pay by usage with no per-seat pricing — so you can deploy anywhere: your cloud, on-premise, GovCloud, or fully air-gapped.
ibl.ai is the part of an AI stack that should never be rented: agents, memory, datasets, evaluation and audit, on infrastructure you control, pointed at whichever model host fits each workload. Agentic OS runs in your cloud account, on-premise or air-gapped, and our Air-Gapped AI and Amazon Bedrock services cover both ends of the hosting spectrum. You own all the code and the data, whichever GPUs serve the tokens.
Full source code under a perpetual license, running on your infrastructure. Not API access to someone else's platform — the stack itself is yours.
Run any LLM — Claude, GPT, Gemini, Llama, Command, or your own fine-tune — and switch providers without rewriting the platform.
Usage-based billing against a budget cap you set. Cost tracks what your organization actually uses, not how many people you employ.
Your cloud, your VPC, on-premise, GovCloud, or a fully air-gapped network with no outbound connectivity.
1.6M+ users across 400+ organizations run the platform this way, including NVIDIA, MIT, and Syracuse University.
ibl.ai is family-owned and operated from New York, NY — a U.S.-headquartered, domestically-owned long-term partner, not a vendor that sells licenses and moves on.
Every statement this page makes about Managed Cloud AI Hosting was checked against the primary source below as of 2026-10-20. If something has changed, tell us and we will correct it.
See how ibl.ai deploys AI agents you own and control—on your infrastructure, integrated with your systems.